Deep Insights into Graph Adversarial Learning: An Empirical Study Perspective. - Jintang Li; Zishan Gu; Qibiao Peng; Kun Xu; Liang Chen 001; Zibin Zheng (HBAI@IJCAI, 2020)

A Survey of Adversarial Learning on Graphs. - Liang Chen 001; Jintang Li; Jiaying Peng; Tao Xie; Zengxu Cao; Kun Xu; Xiangnan He 001; Zibin Zheng (CoRR, 2020)

GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software. - Jintang Li; Kun Xu; Liang Chen 001; Zibin Zheng; Xiao Liu 004 (ICSE (Companion Volume), 2021)

Understanding Structural Vulnerability in Graph Convolutional Networks. - Liang Chen 001; Jintang Li; Qibiao Peng; Yang Liu 245; Zibin Zheng; Carl Yang 001 (IJCAI, 2021)

Phishing Scams Detection in Ethereum Transaction Network. - Liang Chen 001; Jiaying Peng; Yang Liu 245; Jintang Li; Fenfang Xie; Zibin Zheng (ACM Trans. Internet Techn., 2021)

Spiking Graph Convolutional Networks. - Zulun Zhu; Jiaying Peng; Jintang Li; Liang Chen 001; Qi Yu 001; Siqiang Luo (IJCAI, 2022)

Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. - Bingzhe Wu; Yatao Bian; Hengtong Zhang; Jintang Li; Junchi Yu; Liang Chen 001; Chaochao Chen 001; Junzhou Huang (KDD, 2022)

Neighboring Backdoor Attacks on Graph Convolutional Network. - Liang Chen 001; Qibiao Peng; Jintang Li; Yang Liu 245; Jiawei Chen 007; Yong Li 008; Zibin Zheng (CoRR, 2022)

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift. - Bingzhe Wu; Jintang Li; Chengbin Hou; Guoji Fu; Yatao Bian; Liang Chen 001; Junzhou Huang (CoRR, 2022)

GUARD: Graph Universal Adversarial Defense. - Jintang Li; Jie Liao; Ruofan Wu; Liang Chen 001; Changhua Meng; Zibin Zheng; Weiqiang Wang 002 (CoRR, 2022)

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. - Bingzhe Wu; Jintang Li; Junchi Yu; Yatao Bian; Hengtong Zhang; Chaochao Chen 001; Chengbin Hou; Guoji Fu; Liang Chen 001; Tingyang Xu; Yu Rong 001; Xiaolin Zheng; et al. (CoRR, 2022)

MaskGAE: Masked Graph Modeling Meets Graph Autoencoders. - Jintang Li; Ruofan Wu; Wangbin Sun; Liang Chen 001; Sheng Tian; Liang Zhu; Changhua Meng; Zibin Zheng; Weiqiang Wang 002 (CoRR, 2022)

Are All Edges Necessary? A Unified Framework for Graph Purification. - Zishan Gu; Jintang Li; Liang Chen 001 (CoRR, 2022)

Graph Enhanced Neural Interaction Model for recommendation. - Liang Chen 001; Tao Xie; Jintang Li; Zibin Zheng (Knowl. Based Syst., 2022)

Unifying multi-associations through hypergraph for bundle recommendation. - Zhouxin Yu; Jintang Li; Liang Chen 001; Zibin Zheng (Knowl. Based Syst., 2022)

Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks. - Jintang Li; Zhouxin Yu; Zulun Zhu; Liang Chen 001; Qi Yu 001; Zibin Zheng; Sheng Tian; Ruofan Wu; Changhua Meng (AAAI, 2023)

GUARD: Graph Universal Adversarial Defense. - Jintang Li; Jie Liao; Ruofan Wu; Liang Chen 001; Zibin Zheng; Jiawang Dan; Changhua Meng; Weiqiang Wang 002 (CIKM, 2023)

SAILOR: Structural Augmentation Based Tail Node Representation Learning. - Jie Liao; Jintang Li; Liang Chen 001; Bingzhe Wu; Yatao Bian; Zibin Zheng (CIKM, 2023)

Enhancing Graph Collaborative Filtering via Neighborhood Structure Embedding. - Xinzhou Jin; Jintang Li; Yuanzhen Xie; Liang Chen 001; Beibei Kong; Lei Cheng 005; Bo Hu; Zang Li; Zibin Zheng (ICDM, 2023)

SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs. - Sheng Tian; Jihai Dong; Jintang Li; Wenlong Zhao 013; Xiaolong Xu; Baokun Wang; Bowen Song; Changhua Meng; Tianyi Zhang; Liang Chen 001 (IJCAI, 2023)

What's Behind the Mask: Understanding Masked Graph Modeling for Graph Autoencoders. - Jintang Li; Ruofan Wu; Wangbin Sun; Liang Chen 001; Sheng Tian; Liang Zhu; Changhua Meng; Zibin Zheng; Weiqiang Wang 002 (KDD, 2023)

Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs. - Jintang Li; Sheng Tian; Ruofan Wu; Liang Zhu; Wenlong Zhao 013; Changhua Meng; Liang Chen 001; Zibin Zheng; Hongzhi Yin (CoRR, 2023)

Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning. - Jintang Li; Wangbin Sun; Ruofan Wu; Yuchang Zhu; Liang Chen 001; Zibin Zheng (CoRR, 2023)

Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs. - Jintang Li; Zheng Wei; Jiawang Dan; Jing Zhou; Yuchang Zhu; Ruofan Wu; Baokun Wang; Zhang Zhen; Changhua Meng; Hong Jin; Zibin Zheng; Liang Chen 001 (CoRR, 2023)

LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning. - Jintang Li; Jiawang Dan; Ruofan Wu; Jing Zhou; Sheng Tian; Yunfei Liu; Baokun Wang; Changhua Meng; Weiqiang Wang 002; Yuchang Zhu; Liang Chen 001; Zibin Zheng (CoRR, 2023)

Adversarial Attack on Large Scale Graph. - Jintang Li; Tao Xie; Liang Chen 001; Fenfang Xie; Xiangnan He 001; Zibin Zheng (IEEE Trans. Knowl. Data Eng., 2023)

Spectral Adversarial Training for Robust Graph Neural Network. - Jintang Li; Jiaying Peng; Liang Chen 001; Zibin Zheng; Tingting Liang; Qing Ling 001 (IEEE Trans. Knowl. Data Eng., 2023)

A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks. - Jintang Li; Huizhe Zhang; Ruofan Wu; Zulun Zhu; Baokun Wang; Changhua Meng; Zibin Zheng; Liang Chen 001 (ICLR, 2024)

Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective. - Yunfei Liu; Jintang Li; Yuehe Chen; Ruofan Wu; Ericbk Wang; Jing Zhou; Sheng Tian; Shuheng Shen; Xing Fu; Changhua Meng; Weiqiang Wang 002; Liang Chen 001 (KDD, 2024)

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes. - Yuchang Zhu; Jintang Li; Yatao Bian; Zibin Zheng; Liang Chen 001 (KDD, 2024)

Topology-monitorable Contrastive Learning on Dynamic Graphs. - Zulun Zhu; Kai Wang 057; Haoyu Liu; Jintang Li; Siqiang Luo (KDD, 2024)

State Space Models on Temporal Graphs: A First-Principles Study. - Jintang Li; Ruofan Wu; Xinzhou Jin; Boqun Ma; Liang Chen 001; Zibin Zheng (NeurIPS, 2024)

Rethinking and Simplifying Bootstrapped Graph Latents. - Wangbin Sun; Jintang Li; Liang Chen 001; Bingzhe Wu; Yatao Bian; Zibin Zheng (WSDM, 2024)

The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation. - Yuchang Zhu; Jintang Li; Liang Chen 001; Zibin Zheng (WSDM, 2024)

Fair Graph Representation Learning via Sensitive Attribute Disentanglement. - Yuchang Zhu; Jintang Li; Zibin Zheng; Liang Chen 001 (WWW, 2024)

SGHormer: An Energy-Saving Graph Transformer Driven by Spikes. - Huizhe Zhang; Jintang Li; Liang Chen 001; Zibin Zheng (CoRR, 2024)

L2CL: Embarrassingly Simple Layer-to-Layer Contrastive Learning for Graph Collaborative Filtering. - Xinzhou Jin; Jintang Li; Liang Chen 001; Chenyun Yu; Yuanzhen Xie; Tao Xie; Chengxiang Zhuo; Zang Li; Zibin Zheng (CoRR, 2024)

Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective. - Jintang Li; Ruofan Wu; Yuchang Zhu; Huizhe Zhang; Xinzhou Jin; Guibin Zhang; Zulun Zhu; Zibin Zheng; Liang Chen 001 (CoRR, 2024)

FairAGG: Toward Fair Graph Neural Networks via Fair Aggregation. - Yuchang Zhu; Jintang Li; Liang Chen 001; Zibin Zheng (IEEE Trans. Comput. Soc. Syst., 2024)

AP-Net: Semi-Supervised Ultrasound Cardiac Segmentation Using Enhanced Anatomical Prior. - Yuhuan Lu; Jintang Li; Jianxin Lin; Ying Yuan; Jagath C. Rajapakse; Ningbo Zhu; Chunlian Wang; Kenli Li 001 (ICASSP, 2025)

A rule-guided interpretable lightweight framework for fetal standard ultrasound plane capture and biometric measurement. - Jintang Li; Zhan Gao; Chunlian Wang; Bin Pu; Kenli Li 001 (Neurocomputing, 2025)

Capturing latent evolution in dynamic graph: A dual-view architecture from spectral perspective. - Qianqian Chen; Jintang Li; Liang Chen 001 (Knowl. Based Syst., 2025)

Long-term evolutionary patterns matter: Self-supervised anomaly detection on dynamic graphs. - Yun Fu; Che Zhou; Jintang Li; Liang Chen 001 (Knowl. Based Syst., 2025)

FairDLA: Improving the fairness-utility trade-off in graph neural networks via dual-level alignment. - Ying Zhen; Yuchang Zhu; Jintang Li; Liang Chen 001 (Knowl. Based Syst., 2025)

Measuring Diversity in Synthetic Datasets. - Yuchang Zhu; Huizhe Zhang; Bingzhe Wu; Jintang Li; Zibin Zheng; Peilin Zhao; Liang Chen 001; Yatao Bian (CoRR, 2025)

Are Large Language Models In-Context Graph Learners? - Jintang Li; Ruofan Wu; Yuchang Zhu; Huizhe Zhang; Liang Chen 001; Zibin Zheng (CoRR, 2025)

GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization. - Huizhe Zhang; Jintang Li; Yuchang Zhu; Liang Chen 001; Zibin Zheng (CoRR, 2025)

SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding. - Yuchang Zhu; Jintang Li; Huizhe Zhang; Liang Chen 001; Zibin Zheng (CoRR, 2025)

Heterophily-Aware Representation Learning on Heterogeneous Graphs. - Jintang Li; Zheng Wei; Yuchang Zhu; Ruofan Wu; Huizhe Zhang; Liang Chen 001; Zibin Zheng (IEEE Trans. Pattern Anal. Mach. Intell., 2025)